Challenges and Opportunities for Large-scale Healthcare Analytic Research

Seminar
Friday, February 01, 2013
11:00am

Heterogeneous and large volume of Electronic Health Records (EHR) data are becoming available in many healthcare institutes, which include diagnosis, procedures, medications, lab results, clinical notes, medical images, genetic information and etc. Such EHR data from millions of patients serve as huge collective memory of doctors and patients over time. How to leverage that EHR data to help caregivers and patients to make better decisions in future? How to use these data to help clinical and pharmaceutical research?My research focuses on developing large-scale algorithms and systems to build and deploy healthcare analytics. First, I will describe our healthcare analytic architecture, which provides an efficient framework for collaborating with various teams. Second, under this framework, I will overview various techniques and their clinical applications that we developed, which covers clinical text mining, patient representation, knowledge+data feature selection, patient similarity analytics, and patient visualization techniques. Finally, I will highlight some current/future work that I am pursuing in this area.

Speaker

Jimeng Sun is a research staff member at IBM TJ Watson Research Center. He leads research projects of medical informatics, especially in applying large-scale predictive and similarity analytics on healthcare applications. Dr. Sun received his B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, and PhD in Computer Science in Carnegie Mellon University in 2007. His advisor was Prof. Christos Faloutsos. Dr. Sun has extensive research track records on core and applied data mining research: specialized in big data analytics, similarity metric learning, social network analysis, predictive modeling and visual analytics. He has published over 70 papers, filed over 20 patents (4 granted). He has received ICDM best research paper in 2007, SDM best research paper in 2007, and KDD Dissertation runner-up award in 2008.